{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "991a6567",
   "metadata": {},
   "source": [
    "## Assessing Market Inefficiency Based on Volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9aa1d0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import Markdown, display\n",
    "from openbb import obb\n",
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4eafb8ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "obb.user.preferences.output_type = \"dataframe\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458d4906",
   "metadata": {},
   "source": [
    "Define the stock symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce11f992",
   "metadata": {},
   "outputs": [],
   "source": [
    "symbols = [\"NEM\", \"RGLD\", \"SSRM\", \"CDE\", \"LLY\", \"UNH\", \"JNJ\", \"MRK\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a16746",
   "metadata": {},
   "source": [
    "Fetch historical price data for the defined symbols from 2015-01-01 to 2022-12-31 using the \"yfinance\" provider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edbe61c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = obb.equity.price.historical(\n",
    "    symbols, start_date=\"2015-01-01\", end_date=\"2022-12-31\", provider=\"yfinance\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c5b10a",
   "metadata": {},
   "source": [
    "Select relevant columns from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ef2323b",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = data[[\"high\", \"low\", \"close\", \"volume\", \"symbol\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080ad9ec",
   "metadata": {},
   "source": [
    "Filter out symbols with less than 2 years of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "708df9f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "nobs = prices.groupby(\"symbol\").size()\n",
    "mask = nobs[nobs > 2 * 12 * 21].index\n",
    "prices = prices[prices.symbol.isin(mask)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c03a1d6",
   "metadata": {},
   "source": [
    "Reorder levels and sort index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40964487",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = (\n",
    "    prices.set_index(\"symbol\", append=True)\n",
    "    .reorder_levels([\"symbol\", \"date\"])\n",
    "    .sort_index(level=0)\n",
    ").drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af5c973b",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define a function to calculate Parkinson volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2d017eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parkinson(data, window=14, trading_days=252):\n",
    "    rs = (1.0 / (4.0 * np.log(2.0))) * ((data.high / data.low).apply(np.log)) ** 2.0\n",
    "\n",
    "    def f(v):\n",
    "        return (trading_days * v.mean()) ** 0.5\n",
    "\n",
    "    result = rs.rolling(window=window, center=False).apply(func=f)\n",
    "\n",
    "    return result.sub(result.mean()).div(result.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c2032a",
   "metadata": {},
   "source": [
    "Calculate volatility using the Parkinson method and add it to the 'prices' DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1864235",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices[\"vol\"] = prices.groupby(\"symbol\", group_keys=False).apply(parkinson)\n",
    "prices.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17117150",
   "metadata": {},
   "source": [
    "Calculate lagged returns and targets for different time periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2d1f247",
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = [1, 5, 10, 21, 42, 63]\n",
    "for lag in lags:\n",
    "    prices[f\"return_{lag}d\"] = prices.groupby(level=\"symbol\").close.pct_change(lag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "df0045ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in [1, 5, 10, 21, 42, 63]:\n",
    "    prices[f\"target_{t}d\"] = prices.groupby(level=\"symbol\")[f\"return_{t}d\"].shift(-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5146a867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>vol</th>\n",
       "      <th>return_1d</th>\n",
       "      <th>return_5d</th>\n",
       "      <th>return_10d</th>\n",
       "      <th>return_21d</th>\n",
       "      <th>return_42d</th>\n",
       "      <th>return_63d</th>\n",
       "      <th>target_1d</th>\n",
       "      <th>target_5d</th>\n",
       "      <th>target_10d</th>\n",
       "      <th>target_21d</th>\n",
       "      <th>target_42d</th>\n",
       "      <th>target_63d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CDE</th>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>6.430000</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>6.310000</td>\n",
       "      <td>3404900</td>\n",
       "      <td>0.502345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.050713</td>\n",
       "      <td>-0.058637</td>\n",
       "      <td>0.145800</td>\n",
       "      <td>-0.083994</td>\n",
       "      <td>-0.133122</td>\n",
       "      <td>-0.118859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>6.250000</td>\n",
       "      <td>5.930000</td>\n",
       "      <td>5.990000</td>\n",
       "      <td>2234000</td>\n",
       "      <td>0.466274</td>\n",
       "      <td>-0.050713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020033</td>\n",
       "      <td>0.051753</td>\n",
       "      <td>0.170284</td>\n",
       "      <td>-0.083472</td>\n",
       "      <td>-0.118531</td>\n",
       "      <td>-0.136895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>6.130000</td>\n",
       "      <td>5.690000</td>\n",
       "      <td>6.110000</td>\n",
       "      <td>2149800</td>\n",
       "      <td>0.512970</td>\n",
       "      <td>0.020033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.034370</td>\n",
       "      <td>0.045826</td>\n",
       "      <td>0.189853</td>\n",
       "      <td>-0.094926</td>\n",
       "      <td>-0.147300</td>\n",
       "      <td>-0.067103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>6.420000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>6.320000</td>\n",
       "      <td>3697700</td>\n",
       "      <td>0.508157</td>\n",
       "      <td>0.034370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.052215</td>\n",
       "      <td>0.036392</td>\n",
       "      <td>0.136076</td>\n",
       "      <td>-0.115506</td>\n",
       "      <td>-0.202532</td>\n",
       "      <td>-0.106013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>6.360000</td>\n",
       "      <td>5.890000</td>\n",
       "      <td>5.990000</td>\n",
       "      <td>2668300</td>\n",
       "      <td>0.488355</td>\n",
       "      <td>-0.052215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008347</td>\n",
       "      <td>0.188648</td>\n",
       "      <td>0.158598</td>\n",
       "      <td>-0.025042</td>\n",
       "      <td>-0.191987</td>\n",
       "      <td>-0.086811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">UNH</th>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>531.309998</td>\n",
       "      <td>522.900024</td>\n",
       "      <td>531.309998</td>\n",
       "      <td>1292300</td>\n",
       "      <td>-0.477275</td>\n",
       "      <td>0.008006</td>\n",
       "      <td>0.014531</td>\n",
       "      <td>-0.014633</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>-0.016493</td>\n",
       "      <td>0.045145</td>\n",
       "      <td>0.001280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>535.840027</td>\n",
       "      <td>529.849976</td>\n",
       "      <td>531.989990</td>\n",
       "      <td>1596700</td>\n",
       "      <td>-0.480026</td>\n",
       "      <td>0.001280</td>\n",
       "      <td>0.016024</td>\n",
       "      <td>-0.025409</td>\n",
       "      <td>-0.010472</td>\n",
       "      <td>-0.020583</td>\n",
       "      <td>0.046462</td>\n",
       "      <td>-0.006654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>538.150024</td>\n",
       "      <td>527.729980</td>\n",
       "      <td>528.450012</td>\n",
       "      <td>1694200</td>\n",
       "      <td>-0.429139</td>\n",
       "      <td>-0.006654</td>\n",
       "      <td>0.015840</td>\n",
       "      <td>-0.018152</td>\n",
       "      <td>-0.007177</td>\n",
       "      <td>-0.024640</td>\n",
       "      <td>0.028233</td>\n",
       "      <td>0.002706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>533.679993</td>\n",
       "      <td>528.859985</td>\n",
       "      <td>529.880005</td>\n",
       "      <td>1379700</td>\n",
       "      <td>-0.450629</td>\n",
       "      <td>0.002706</td>\n",
       "      <td>0.004436</td>\n",
       "      <td>-0.015752</td>\n",
       "      <td>0.003561</td>\n",
       "      <td>-0.038749</td>\n",
       "      <td>0.041369</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>530.500000</td>\n",
       "      <td>524.840027</td>\n",
       "      <td>530.179993</td>\n",
       "      <td>1849600</td>\n",
       "      <td>-0.491592</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.004738</td>\n",
       "      <td>-0.032094</td>\n",
       "      <td>-0.044979</td>\n",
       "      <td>0.049778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16008 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         high         low       close   volume       vol  \\\n",
       "symbol date                                                                \n",
       "CDE    2015-01-22    6.430000    6.150000    6.310000  3404900  0.502345   \n",
       "       2015-01-23    6.250000    5.930000    5.990000  2234000  0.466274   \n",
       "       2015-01-26    6.130000    5.690000    6.110000  2149800  0.512970   \n",
       "       2015-01-27    6.420000    6.120000    6.320000  3697700  0.508157   \n",
       "       2015-01-28    6.360000    5.890000    5.990000  2668300  0.488355   \n",
       "...                       ...         ...         ...      ...       ...   \n",
       "UNH    2022-12-23  531.309998  522.900024  531.309998  1292300 -0.477275   \n",
       "       2022-12-27  535.840027  529.849976  531.989990  1596700 -0.480026   \n",
       "       2022-12-28  538.150024  527.729980  528.450012  1694200 -0.429139   \n",
       "       2022-12-29  533.679993  528.859985  529.880005  1379700 -0.450629   \n",
       "       2022-12-30  530.500000  524.840027  530.179993  1849600 -0.491592   \n",
       "\n",
       "                   return_1d  return_5d  return_10d  return_21d  return_42d  \\\n",
       "symbol date                                                                   \n",
       "CDE    2015-01-22        NaN        NaN         NaN         NaN         NaN   \n",
       "       2015-01-23  -0.050713        NaN         NaN         NaN         NaN   \n",
       "       2015-01-26   0.020033        NaN         NaN         NaN         NaN   \n",
       "       2015-01-27   0.034370        NaN         NaN         NaN         NaN   \n",
       "       2015-01-28  -0.052215        NaN         NaN         NaN         NaN   \n",
       "...                      ...        ...         ...         ...         ...   \n",
       "UNH    2022-12-23   0.008006   0.014531   -0.014633    0.003020   -0.016493   \n",
       "       2022-12-27   0.001280   0.016024   -0.025409   -0.010472   -0.020583   \n",
       "       2022-12-28  -0.006654   0.015840   -0.018152   -0.007177   -0.024640   \n",
       "       2022-12-29   0.002706   0.004436   -0.015752    0.003561   -0.038749   \n",
       "       2022-12-30   0.000566   0.005862    0.004738   -0.032094   -0.044979   \n",
       "\n",
       "                   return_63d  target_1d  target_5d  target_10d  target_21d  \\\n",
       "symbol date                                                                   \n",
       "CDE    2015-01-22         NaN  -0.050713  -0.058637    0.145800   -0.083994   \n",
       "       2015-01-23         NaN   0.020033   0.051753    0.170284   -0.083472   \n",
       "       2015-01-26         NaN   0.034370   0.045826    0.189853   -0.094926   \n",
       "       2015-01-27         NaN  -0.052215   0.036392    0.136076   -0.115506   \n",
       "       2015-01-28         NaN  -0.008347   0.188648    0.158598   -0.025042   \n",
       "...                       ...        ...        ...         ...         ...   \n",
       "UNH    2022-12-23    0.045145   0.001280        NaN         NaN         NaN   \n",
       "       2022-12-27    0.046462  -0.006654        NaN         NaN         NaN   \n",
       "       2022-12-28    0.028233   0.002706        NaN         NaN         NaN   \n",
       "       2022-12-29    0.041369   0.000566        NaN         NaN         NaN   \n",
       "       2022-12-30    0.049778        NaN        NaN         NaN         NaN   \n",
       "\n",
       "                   target_42d  target_63d  \n",
       "symbol date                                \n",
       "CDE    2015-01-22   -0.133122   -0.118859  \n",
       "       2015-01-23   -0.118531   -0.136895  \n",
       "       2015-01-26   -0.147300   -0.067103  \n",
       "       2015-01-27   -0.202532   -0.106013  \n",
       "       2015-01-28   -0.191987   -0.086811  \n",
       "...                       ...         ...  \n",
       "UNH    2022-12-23         NaN         NaN  \n",
       "       2022-12-27         NaN         NaN  \n",
       "       2022-12-28         NaN         NaN  \n",
       "       2022-12-29         NaN         NaN  \n",
       "       2022-12-30         NaN         NaN  \n",
       "\n",
       "[16008 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1383f2c5",
   "metadata": {},
   "source": [
    "Plot a joint plot of volatility vs. 1-day target return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ebbe610a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = \"target_1d\"\n",
    "metric = \"vol\"\n",
    "j = sns.jointplot(x=metric, y=target, data=prices)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b723a5ba",
   "metadata": {},
   "source": [
    "Calculate and display the Spearman rank correlation and p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "452add15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.29% (0.38%)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = prices[[metric, target]].dropna()\n",
    "r, p = spearmanr(df[metric], df[target])\n",
    "display(f\"{r:,.2%} ({p:.2%})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1fc618c",
   "metadata": {},
   "source": [
    "Display the first 10 rows of 'prices' DataFrame for the symbol \"NEM\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27b47012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>vol</th>\n",
       "      <th>return_1d</th>\n",
       "      <th>return_5d</th>\n",
       "      <th>return_10d</th>\n",
       "      <th>return_21d</th>\n",
       "      <th>return_42d</th>\n",
       "      <th>return_63d</th>\n",
       "      <th>target_1d</th>\n",
       "      <th>target_5d</th>\n",
       "      <th>target_10d</th>\n",
       "      <th>target_21d</th>\n",
       "      <th>target_42d</th>\n",
       "      <th>target_63d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>24.690001</td>\n",
       "      <td>23.980000</td>\n",
       "      <td>24.290001</td>\n",
       "      <td>13132700</td>\n",
       "      <td>1.021514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005764</td>\n",
       "      <td>-0.004940</td>\n",
       "      <td>0.021820</td>\n",
       "      <td>0.068752</td>\n",
       "      <td>-0.058872</td>\n",
       "      <td>-0.034582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>24.430000</td>\n",
       "      <td>23.670000</td>\n",
       "      <td>24.150000</td>\n",
       "      <td>12056800</td>\n",
       "      <td>0.976773</td>\n",
       "      <td>-0.005764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014079</td>\n",
       "      <td>0.041408</td>\n",
       "      <td>-0.004141</td>\n",
       "      <td>0.068737</td>\n",
       "      <td>-0.069151</td>\n",
       "      <td>0.034369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>24.629999</td>\n",
       "      <td>23.350000</td>\n",
       "      <td>24.490000</td>\n",
       "      <td>11189200</td>\n",
       "      <td>1.059908</td>\n",
       "      <td>0.014079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026541</td>\n",
       "      <td>0.025316</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>0.063699</td>\n",
       "      <td>-0.095141</td>\n",
       "      <td>0.044916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>25.250000</td>\n",
       "      <td>24.530001</td>\n",
       "      <td>25.139999</td>\n",
       "      <td>10774500</td>\n",
       "      <td>0.948081</td>\n",
       "      <td>0.026541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.035004</td>\n",
       "      <td>-0.020684</td>\n",
       "      <td>-0.029037</td>\n",
       "      <td>0.046539</td>\n",
       "      <td>-0.115752</td>\n",
       "      <td>0.048131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>25.049999</td>\n",
       "      <td>24.030001</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>11251300</td>\n",
       "      <td>0.962112</td>\n",
       "      <td>-0.035004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003710</td>\n",
       "      <td>0.023083</td>\n",
       "      <td>-0.004946</td>\n",
       "      <td>0.085326</td>\n",
       "      <td>-0.092333</td>\n",
       "      <td>0.091509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-29</th>\n",
       "      <td>24.340000</td>\n",
       "      <td>23.420000</td>\n",
       "      <td>24.170000</td>\n",
       "      <td>8850000</td>\n",
       "      <td>1.016856</td>\n",
       "      <td>-0.003710</td>\n",
       "      <td>-0.004940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040546</td>\n",
       "      <td>0.026893</td>\n",
       "      <td>0.014067</td>\n",
       "      <td>0.071163</td>\n",
       "      <td>-0.101779</td>\n",
       "      <td>0.095987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>25.219999</td>\n",
       "      <td>23.920000</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>11594400</td>\n",
       "      <td>1.139381</td>\n",
       "      <td>0.040546</td>\n",
       "      <td>0.041408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001590</td>\n",
       "      <td>-0.043738</td>\n",
       "      <td>-0.014712</td>\n",
       "      <td>0.018688</td>\n",
       "      <td>-0.096620</td>\n",
       "      <td>0.053280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>25.200001</td>\n",
       "      <td>24.559999</td>\n",
       "      <td>25.110001</td>\n",
       "      <td>7497000</td>\n",
       "      <td>1.109351</td>\n",
       "      <td>-0.001590</td>\n",
       "      <td>0.025316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019514</td>\n",
       "      <td>-0.019912</td>\n",
       "      <td>-0.036639</td>\n",
       "      <td>0.003186</td>\n",
       "      <td>-0.110315</td>\n",
       "      <td>0.037435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>25.070000</td>\n",
       "      <td>24.139999</td>\n",
       "      <td>24.620001</td>\n",
       "      <td>9038100</td>\n",
       "      <td>0.957277</td>\n",
       "      <td>-0.019514</td>\n",
       "      <td>-0.020684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.008123</td>\n",
       "      <td>-0.008530</td>\n",
       "      <td>0.004874</td>\n",
       "      <td>0.027620</td>\n",
       "      <td>-0.082859</td>\n",
       "      <td>0.054021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>25.030001</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>24.820000</td>\n",
       "      <td>7071100</td>\n",
       "      <td>0.708258</td>\n",
       "      <td>0.008123</td>\n",
       "      <td>0.023083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.027397</td>\n",
       "      <td>-0.014504</td>\n",
       "      <td>-0.061241</td>\n",
       "      <td>-0.106769</td>\n",
       "      <td>0.033441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low      close    volume       vol  return_1d  \\\n",
       "date                                                                         \n",
       "2015-01-22  24.690001  23.980000  24.290001  13132700  1.021514        NaN   \n",
       "2015-01-23  24.430000  23.670000  24.150000  12056800  0.976773  -0.005764   \n",
       "2015-01-26  24.629999  23.350000  24.490000  11189200  1.059908   0.014079   \n",
       "2015-01-27  25.250000  24.530001  25.139999  10774500  0.948081   0.026541   \n",
       "2015-01-28  25.049999  24.030001  24.260000  11251300  0.962112  -0.035004   \n",
       "2015-01-29  24.340000  23.420000  24.170000   8850000  1.016856  -0.003710   \n",
       "2015-01-30  25.219999  23.920000  25.150000  11594400  1.139381   0.040546   \n",
       "2015-02-02  25.200001  24.559999  25.110001   7497000  1.109351  -0.001590   \n",
       "2015-02-03  25.070000  24.139999  24.620001   9038100  0.957277  -0.019514   \n",
       "2015-02-04  25.030001  24.500000  24.820000   7071100  0.708258   0.008123   \n",
       "\n",
       "            return_5d  return_10d  return_21d  return_42d  return_63d  \\\n",
       "date                                                                    \n",
       "2015-01-22        NaN         NaN         NaN         NaN         NaN   \n",
       "2015-01-23        NaN         NaN         NaN         NaN         NaN   \n",
       "2015-01-26        NaN         NaN         NaN         NaN         NaN   \n",
       "2015-01-27        NaN         NaN         NaN         NaN         NaN   \n",
       "2015-01-28        NaN         NaN         NaN         NaN         NaN   \n",
       "2015-01-29  -0.004940         NaN         NaN         NaN         NaN   \n",
       "2015-01-30   0.041408         NaN         NaN         NaN         NaN   \n",
       "2015-02-02   0.025316         NaN         NaN         NaN         NaN   \n",
       "2015-02-03  -0.020684         NaN         NaN         NaN         NaN   \n",
       "2015-02-04   0.023083         NaN         NaN         NaN         NaN   \n",
       "\n",
       "            target_1d  target_5d  target_10d  target_21d  target_42d  \\\n",
       "date                                                                   \n",
       "2015-01-22  -0.005764  -0.004940    0.021820    0.068752   -0.058872   \n",
       "2015-01-23   0.014079   0.041408   -0.004141    0.068737   -0.069151   \n",
       "2015-01-26   0.026541   0.025316    0.004900    0.063699   -0.095141   \n",
       "2015-01-27  -0.035004  -0.020684   -0.029037    0.046539   -0.115752   \n",
       "2015-01-28  -0.003710   0.023083   -0.004946    0.085326   -0.092333   \n",
       "2015-01-29   0.040546   0.026893    0.014067    0.071163   -0.101779   \n",
       "2015-01-30  -0.001590  -0.043738   -0.014712    0.018688   -0.096620   \n",
       "2015-02-02  -0.019514  -0.019912   -0.036639    0.003186   -0.110315   \n",
       "2015-02-03   0.008123  -0.008530    0.004874    0.027620   -0.082859   \n",
       "2015-02-04   0.000000  -0.027397   -0.014504   -0.061241   -0.106769   \n",
       "\n",
       "            target_63d  \n",
       "date                    \n",
       "2015-01-22   -0.034582  \n",
       "2015-01-23    0.034369  \n",
       "2015-01-26    0.044916  \n",
       "2015-01-27    0.048131  \n",
       "2015-01-28    0.091509  \n",
       "2015-01-29    0.095987  \n",
       "2015-01-30    0.053280  \n",
       "2015-02-02    0.037435  \n",
       "2015-02-03    0.054021  \n",
       "2015-02-04    0.033441  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = prices.loc[\"NEM\"].head(10)\n",
    "display(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3811141",
   "metadata": {},
   "source": [
    "Display the metric and target columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "321b23c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol  date      \n",
       "CDE     2015-01-22    0.502345\n",
       "        2015-01-23    0.466274\n",
       "        2015-01-26    0.512970\n",
       "        2015-01-27    0.508157\n",
       "        2015-01-28    0.488355\n",
       "                        ...   \n",
       "UNH     2022-12-22   -0.464863\n",
       "        2022-12-23   -0.477275\n",
       "        2022-12-27   -0.480026\n",
       "        2022-12-28   -0.429139\n",
       "        2022-12-29   -0.450629\n",
       "Name: vol, Length: 16000, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "symbol  date      \n",
       "CDE     2015-01-22   -0.050713\n",
       "        2015-01-23    0.020033\n",
       "        2015-01-26    0.034370\n",
       "        2015-01-27   -0.052215\n",
       "        2015-01-28   -0.008347\n",
       "                        ...   \n",
       "UNH     2022-12-22    0.008006\n",
       "        2022-12-23    0.001280\n",
       "        2022-12-27   -0.006654\n",
       "        2022-12-28    0.002706\n",
       "        2022-12-29    0.000566\n",
       "Name: target_1d, Length: 16000, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df[metric])\n",
    "display(df[target])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e9d729",
   "metadata": {},
   "source": [
    "Display the correlation and p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf7b8f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022894821944437795"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.003777781841261211"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(r, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fad08f",
   "metadata": {},
   "source": [
    "**Jason Strimpel** is the founder of <a href='https://pyquantnews.com/'>PyQuant News</a> and co-founder of <a href='https://www.tradeblotter.io/'>Trade Blotter</a>. His career in algorithmic trading spans 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career spans America, Europe, and Asia. He shares his expertise through the <a href='https://pyquantnews.com/subscribe-to-the-pyquant-newsletter/'>PyQuant Newsletter</a>, social media, and has taught over 1,000+ algorithmic trading with Python in his popular course **<a href='https://gettingstartedwithpythonforquantfinance.com/'>Getting Started With Python for Quant Finance</a>**. All code is for educational purposes only. Nothing provided here is financial advise. Use at your own risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19e7d489-2f8e-4f2a-a8a1-c23259019adf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
